





"""simple docstring"""





import collections
from typing import List, Optional, Union

from ...tokenization_utils_base import BatchEncoding
from ...utils import TensorType, add_end_docstrings, add_start_docstrings, logging
from ..bert.tokenization_bert_fast import BertTokenizerFast
from .tokenization_dpr import DPRContextEncoderTokenizer, DPRQuestionEncoderTokenizer, DPRReaderTokenizer


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =  {"vocab_file": "vocab.txt", "tokenizer_file": "tokenizer.json"}

SCREAMING_SNAKE_CASE__   =  {
    "vocab_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-ctx_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-ctx_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-ctx_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
SCREAMING_SNAKE_CASE__   =  {
    "vocab_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-question_encoder-single-nq-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-question_encoder-multiset-base": (
            "https://huggingface.co/facebook/dpr-question_encoder-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}
SCREAMING_SNAKE_CASE__   =  {
    "vocab_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/vocab.txt"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/vocab.txt"
        ),
    },
    "tokenizer_file": {
        "facebook/dpr-reader-single-nq-base": (
            "https://huggingface.co/facebook/dpr-reader-single-nq-base/resolve/main/tokenizer.json"
        ),
        "facebook/dpr-reader-multiset-base": (
            "https://huggingface.co/facebook/dpr-reader-multiset-base/resolve/main/tokenizer.json"
        ),
    },
}

SCREAMING_SNAKE_CASE__   =  {
    "facebook/dpr-ctx_encoder-single-nq-base": 512,
    "facebook/dpr-ctx_encoder-multiset-base": 512,
}
SCREAMING_SNAKE_CASE__   =  {
    "facebook/dpr-question_encoder-single-nq-base": 512,
    "facebook/dpr-question_encoder-multiset-base": 512,
}
SCREAMING_SNAKE_CASE__   =  {
    "facebook/dpr-reader-single-nq-base": 512,
    "facebook/dpr-reader-multiset-base": 512,
}


SCREAMING_SNAKE_CASE__   =  {
    "facebook/dpr-ctx_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-ctx_encoder-multiset-base": {"do_lower_case": True},
}
SCREAMING_SNAKE_CASE__   =  {
    "facebook/dpr-question_encoder-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-question_encoder-multiset-base": {"do_lower_case": True},
}
SCREAMING_SNAKE_CASE__   =  {
    "facebook/dpr-reader-single-nq-base": {"do_lower_case": True},
    "facebook/dpr-reader-multiset-base": {"do_lower_case": True},
}

class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE							      = CONTEXT_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE							      = CONTEXT_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE							      = CONTEXT_ENCODER_PRETRAINED_INIT_CONFIGURATION
      _SCREAMING_SNAKE_CASE							      = DPRContextEncoderTokenizer







class 						lowercase   (   _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE							      = QUESTION_ENCODER_PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE							      = QUESTION_ENCODER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE							      = QUESTION_ENCODER_PRETRAINED_INIT_CONFIGURATION
      _SCREAMING_SNAKE_CASE							      = DPRQuestionEncoderTokenizer







SCREAMING_SNAKE_CASE__   =  collections.namedtuple(
    "DPRSpanPrediction", ["span_score", "relevance_score", "doc_id", "start_index", "end_index", "text"]
)

SCREAMING_SNAKE_CASE__   =  collections.namedtuple("DPRReaderOutput", ["start_logits", "end_logits", "relevance_logits"])


SCREAMING_SNAKE_CASE__   =  r"\n    Return a dictionary with the token ids of the input strings and other information to give to `.decode_best_spans`.\n    It converts the strings of a question and different passages (title and text) in a sequence of IDs (integers),\n    using the tokenizer and vocabulary. The resulting `input_ids` is a matrix of size `(n_passages, sequence_length)`\n    with the format:\n\n    [CLS] <question token ids> [SEP] <titles ids> [SEP] <texts ids>\n\n    Args:\n        questions (`str` or `List[str]`):\n            The questions to be encoded. You can specify one question for many passages. In this case, the question\n            will be duplicated like `[questions] * n_passages`. Otherwise you have to specify as many questions as in\n            `titles` or `texts`.\n        titles (`str` or `List[str]`):\n            The passages titles to be encoded. This can be a string or a list of strings if there are several passages.\n        texts (`str` or `List[str]`):\n            The passages texts to be encoded. This can be a string or a list of strings if there are several passages.\n        padding (`bool`, `str` or [`~utils.PaddingStrategy`], *optional*, defaults to `False`):\n            Activates and controls padding. Accepts the following values:\n\n            - `True` or `'longest'`: Pad to the longest sequence in the batch (or no padding if only a single sequence\n              if provided).\n            - `'max_length'`: Pad to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided.\n            - `False` or `'do_not_pad'` (default): No padding (i.e., can output a batch with sequences of different\n              lengths).\n        truncation (`bool`, `str` or [`~tokenization_utils_base.TruncationStrategy`], *optional*, defaults to `False`):\n            Activates and controls truncation. Accepts the following values:\n\n            - `True` or `'longest_first'`: Truncate to a maximum length specified with the argument `max_length` or to\n              the maximum acceptable input length for the model if that argument is not provided. This will truncate\n              token by token, removing a token from the longest sequence in the pair if a pair of sequences (or a batch\n              of pairs) is provided.\n            - `'only_first'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the first\n              sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `'only_second'`: Truncate to a maximum length specified with the argument `max_length` or to the maximum\n              acceptable input length for the model if that argument is not provided. This will only truncate the\n              second sequence of a pair if a pair of sequences (or a batch of pairs) is provided.\n            - `False` or `'do_not_truncate'` (default): No truncation (i.e., can output batch with sequence lengths\n              greater than the model maximum admissible input size).\n        max_length (`int`, *optional*):\n                Controls the maximum length to use by one of the truncation/padding parameters.\n\n                If left unset or set to `None`, this will use the predefined model maximum length if a maximum length\n                is required by one of the truncation/padding parameters. If the model has no specific maximum input\n                length (like XLNet) truncation/padding to a maximum length will be deactivated.\n        return_tensors (`str` or [`~utils.TensorType`], *optional*):\n                If set, will return tensors instead of list of python integers. Acceptable values are:\n\n                - `'tf'`: Return TensorFlow `tf.constant` objects.\n                - `'pt'`: Return PyTorch `torch.Tensor` objects.\n                - `'np'`: Return Numpy `np.ndarray` objects.\n        return_attention_mask (`bool`, *optional*):\n            Whether or not to return the attention mask. If not set, will return the attention mask according to the\n            specific tokenizer's default, defined by the `return_outputs` attribute.\n\n            [What are attention masks?](../glossary#attention-mask)\n\n    Return:\n        `Dict[str, List[List[int]]]`: A dictionary with the following keys:\n\n        - `input_ids`: List of token ids to be fed to a model.\n        - `attention_mask`: List of indices specifying which tokens should be attended to by the model.\n    "

@add_start_docstrings(_UpperCAmelCase   )
class 						lowercase   :





      def __call__(			self ,		lowercase ,		lowercase = None ,		lowercase = None ,		lowercase = False ,		lowercase = False ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		**lowercase ,		)						->	BatchEncoding:
            if titles is None and texts is None:
                  return super().__call__(
                      lowercase ,		padding=lowercase ,		truncation=lowercase ,		max_length=lowercase ,		return_tensors=lowercase ,		return_attention_mask=lowercase ,		**lowercase ,		)
            elif titles is None or texts is None:
                  lowerCAmelCase    		=			titles if texts is None else texts
                  return super().__call__(
                      lowercase ,		lowercase ,		padding=lowercase ,		truncation=lowercase ,		max_length=lowercase ,		return_tensors=lowercase ,		return_attention_mask=lowercase ,		**lowercase ,		)
            lowerCAmelCase    		=			titles if not isinstance(lowercase ,		lowercase					) else [titles]
            lowerCAmelCase    		=			texts if not isinstance(lowercase ,		lowercase					) else [texts]
            lowerCAmelCase    		=			len(lowercase					)
            lowerCAmelCase    		=			questions if not isinstance(lowercase ,		lowercase					) else [questions] * n_passages
            assert len(lowercase					) == len(
                lowercase					), f'There should be as many titles than texts but got {len(lowercase					)} titles and {len(lowercase					)} texts.'
            lowerCAmelCase    		=			super().__call__(lowercase ,		lowercase ,		padding=lowercase ,		truncation=lowercase					)["""input_ids"""]
            lowerCAmelCase    		=			super().__call__(lowercase ,		add_special_tokens=lowercase ,		padding=lowercase ,		truncation=lowercase					)["""input_ids"""]
            lowerCAmelCase    		=			{
                """input_ids""": [
                    (encoded_question_and_title + encoded_text)[:max_length]
                    if max_length is not None and truncation
                    else encoded_question_and_title + encoded_text
                    for encoded_question_and_title, encoded_text in zip(lowercase ,		lowercase					)
                ]
            }
            if return_attention_mask is not False:
                  lowerCAmelCase    		=			[]
                  for input_ids in encoded_inputs["input_ids"]:
                        attention_mask.append([int(input_id != self.pad_token_id					) for input_id in input_ids]					)
                  lowerCAmelCase    		=			attention_mask
            return self.pad(lowercase ,		padding=lowercase ,		max_length=lowercase ,		return_tensors=lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase = 16 ,		lowercase = 64 ,		lowercase = 4 ,		)						->	List[DPRSpanPrediction]:
            lowerCAmelCase    		=			reader_input["""input_ids"""]
            lowerCAmelCase		,						lowerCAmelCase		,						lowerCAmelCase    		=			reader_output[:3]
            lowerCAmelCase    		=			len(lowercase					)
            lowerCAmelCase    		=			sorted(range(lowercase					) ,		reverse=lowercase ,		key=relevance_logits.__getitem__					)
            lowerCAmelCase    		=			[]
            for doc_id in sorted_docs:
                  lowerCAmelCase    		=			list(input_ids[doc_id]					)
                  # assuming question & title information is at the beginning of the sequence
                  lowerCAmelCase    		=			sequence_ids.index(self.sep_token_id ,		2					) + 1  # second sep id
                  if sequence_ids[-1] == self.pad_token_id:
                        lowerCAmelCase    		=			sequence_ids.index(self.pad_token_id					)
                  else:
                        lowerCAmelCase    		=			len(lowercase					)

                  lowerCAmelCase    		=			self._get_best_spans(
                      start_logits=start_logits[doc_id][passage_offset:sequence_len] ,		end_logits=end_logits[doc_id][passage_offset:sequence_len] ,		max_answer_length=lowercase ,		top_spans=lowercase ,		)
                  for start_index, end_index in best_spans:
                        start_index += passage_offset
                        end_index += passage_offset
                        nbest_spans_predictions.append(
                            DPRSpanPrediction(
                                span_score=start_logits[doc_id][start_index] + end_logits[doc_id][end_index] ,		relevance_score=relevance_logits[doc_id] ,		doc_id=lowercase ,		start_index=lowercase ,		end_index=lowercase ,		text=self.decode(sequence_ids[start_index : end_index + 1]					) ,		)					)
                  if len(lowercase					) >= num_spans:
                        break
            return nbest_spans_predictions[:num_spans]





      def        _snake_case     (			self ,		lowercase ,		lowercase ,		lowercase ,		lowercase ,		)						->	List[DPRSpanPrediction]:
            lowerCAmelCase    		=			[]
            for start_index, start_score in enumerate(lowercase					):
                  for answer_length, end_score in enumerate(end_logits[start_index : start_index + max_answer_length]					):
                        scores.append(((start_index, start_index + answer_length), start_score + end_score)					)
            lowerCAmelCase    		=			sorted(lowercase ,		key=lambda lowercase					: x[1] ,		reverse=lowercase					)
            lowerCAmelCase    		=			[]
            for (start_index, end_index), score in scores:
                  assert start_index <= end_index, f'Wrong span indices: [{start_index}:{end_index}]'
                  lowerCAmelCase    		=			end_index - start_index + 1
                  assert length <= max_answer_length, f'Span is too long: {length} > {max_answer_length}'
                  if any(
                      start_index <= prev_start_index <= prev_end_index <= end_index
                      or prev_start_index <= start_index <= end_index <= prev_end_index
                      for (prev_start_index, prev_end_index) in chosen_span_intervals					):
                        continue
                  chosen_span_intervals.append((start_index, end_index)					)

                  if len(lowercase					) == top_spans:
                        break
            return chosen_span_intervals







@add_end_docstrings(_UpperCAmelCase   )
class 						lowercase   (   _UpperCAmelCase	,    _UpperCAmelCase   ):
      _SCREAMING_SNAKE_CASE							      = VOCAB_FILES_NAMES
      _SCREAMING_SNAKE_CASE							      = READER_PRETRAINED_VOCAB_FILES_MAP
      _SCREAMING_SNAKE_CASE							      = READER_PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
      _SCREAMING_SNAKE_CASE							      = READER_PRETRAINED_INIT_CONFIGURATION
      _SCREAMING_SNAKE_CASE							      = ['input_ids', 'attention_mask']
      _SCREAMING_SNAKE_CASE							      = DPRReaderTokenizer













"""simple docstring"""





def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		int				,		SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




      if a < 0 or b < 0:
            raise ValueError("""the value of both inputs must be positive"""     )
      lowerCAmelCase    		=			str(bin(SCREAMING_SNAKE_CASE     )     )[2:]  # remove the leading "0b"
      lowerCAmelCase    		=			str(bin(SCREAMING_SNAKE_CASE     )     )[2:]
      lowerCAmelCase    		=			max(len(SCREAMING_SNAKE_CASE     )				,		len(SCREAMING_SNAKE_CASE     )     )
      return "0b" + "".join(
          str(int("""1""" in (char_a, char_b)     )     )
          for char_a, char_b in zip(a_binary.zfill(SCREAMING_SNAKE_CASE     )				,		b_binary.zfill(SCREAMING_SNAKE_CASE     )     )     )


if __name__ == "__main__":
       import doctest

       doctest.testmod()






